A hybrid model for lane change prediction with V2X-based driver assistance

Ting Xu, Ruisen Jiang, Changlei Wen, Meijun Liu and Jiehan Zhou

Physica A: Statistical Mechanics and its Applications, 2019, vol. 534, issue C

Abstract: This paper proposes a hybrid model for lane-change prediction, with V2X-based driver assistance (HM4LCP) for improving driving safety. We carried out experiments in the testbed of Chang’an University to investigate driving behaviors on an urban road. 412 lane-changing units and 824 lane-keeping units were obtained from the vehicle trajectories. The HM4LCP integrates the Gaussian Mixture Model and the Continuous Hidden Markov Model to identify and predict lane-changing behaviors within a V2X environment. We collected 412 lane-changing samples and randomly chose 140, in order to test the HM4LCP’s identification accuracy. The results show that the HM4LCP results in an accuracy as high as 93.6% for lane-changing and 90.4% for lane-keeping, respectively. The proposed HM4LCP method can be applied to the driving assistant systems for lane-change warnings, in the initial stage.

Keywords: Lane-change prediction; Continuous hidden markov model; Gaussian mixture model; Intelligent vehicle (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119311707

DOI: 10.1016/j.physa.2019.122033

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